Geographically Insensitive Spatial-Temporal POI Recommendation Based on Heterogeneous Graph Embedding

Abstract

The increasingly large scale of location-based social networks (LBSN) promotes the rapid development of point-of-interest (POI) recommendation business. POI geospatial distance directly adopted by traditional methods is difficult to simulate the highly random behavior path of users. And the point-of-interest recommendation process brings sensitivity to the location distance measurement. Meanwhile, the sparse POI check-in data of users in social networks are also easy to have a huge impact on the recommendation accuracy. To solve the above issues, geographically insensitive spatio-temporal POI recommendation model based on heterogeneous graph embedding (GIPR) is proposed. Firstly, the user behavior sequence is introduced to construct the spatial and temporal topological diagram of the behavior POI. The weighted spatial path is used to represent the relative location distance. It can not only conform to the characteristics of user behavior, but also reduce the sensitivity of the recommendation process to the distance between POI, thus enhancing the ability to explain the recommendation results. As for heterogeneous and highly sparse interaction data, the proposed recommendation method can learn the complete LBSN heterogeneous graph from local and global perspectives, and integrate richer user and POI features. Finally, the long-term and short-term preferences of users are extracted through the attention layer to achieve more personalized POI recommendation. Experiments on two large-scale real datasets Foursquare and Gowalla show that GIPR has higher recommendation accuracy and stronger interpretability.